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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Change-Point Detection Using the Conditional Entropy of Ordinal Patterns.

Anton M Unakafov1,2,3,4,5, Karsten Keller1

  • 1Institute of Mathematics, University of Lübeck, 23562 Lübeck, Germany.

Entropy (Basel, Switzerland)
|December 3, 2020
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This study introduces a novel change-point detection method using time series ordinal patterns. The approach effectively identifies shifts in data structure, offering a valuable complement to existing techniques.

Keywords:
change-point detectionconditional entropyordinal pattern

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Area of Science:

  • Time Series Analysis
  • Statistical Signal Processing
  • Data Mining

Background:

  • Traditional change-point detection methods often rely on specific data distributions or assumptions.
  • Detecting changes in the intrinsic structure of time series data is crucial for various applications.
  • Existing methods may not capture subtle shifts in local dynamics.

Purpose of the Study:

  • To develop a novel change-point detection statistic based solely on the ordinal structure of time series.
  • To investigate the performance of a statistic derived from the conditional entropy of ordinal patterns.
  • To introduce a method requiring minimal prior information about the data.

Main Methods:

  • Utilizing ordinal patterns to characterize local trends (ups and downs) within a time series.
  • Developing a statistic based on the conditional entropy of these ordinal patterns.
  • Evaluating the method's performance through numerical experiments.

Main Results:

  • The proposed statistic demonstrates good performance in numerical experiments.
  • The method effectively detects changes in the intrinsic pattern structure of time series.
  • The approach requires minimal a priori information, making it broadly applicable.

Conclusions:

  • The ordinal-based change-point detection method offers a robust alternative for identifying structural shifts.
  • This technique is particularly useful for detecting changes in local dynamics, complementing other methods.
  • The method's minimal data requirements enhance its practical utility in diverse time series analysis scenarios.